nerc.ac.uk

A comparison of partitioning when applied to HF radar and wave model spectra

Waters, Jennifer; Wyatt, Lucy; Wolf, Judith ORCID: https://orcid.org/0000-0003-4129-8221; Hines, Adrian; Holt, Martin. 2008 A comparison of partitioning when applied to HF radar and wave model spectra. In: PECS 2008: Physics of Estuaries and Coastal Seas, Liverpool, UK, 25-29 August 2008. Liverpool, 307-310.

Before downloading, please read NORA policies.
[thumbnail of Wolf_a_comparison_of_partitioning.pdf]
Preview
Text
Wolf_a_comparison_of_partitioning.pdf

Download (946kB) | Preview

Abstract/Summary

It is useful to partition ocean wave spectra for the classification of wind sea and swell and for detecting weaknesses in models or measuring systems. In this study various partitioning scheme have been investigated and their effectiveness, robustness and feasibility for use in automated systems taken into consideration. The partitioning scheme used by Hasselmann et al. (1996) appears to be the most useful for fully automated processes and therefore the performance of the partitioning method has been analyzed by comparisons of spectra from the Wavewatch III model, the Swan model and from HF radar. This comparison suggests that specific processes are needed to manage the effect of noise and sensitivity on the partitioning method when looking at the different spectra. The research has focused on the Celtic sea region for the 2003 to 2005 period when the Pisces radar system was operational. In addition a Directional Waverider buoy was deployed in the area and the buoy’s reconstructed spectra have also been considered.

Item Type: Publication - Conference Item (Paper)
Programmes: Oceans 2025 > Shelf and coastal processes
Additional Keywords: DIRECTIONAL WAVE SPECTRUM; PARTITIONING; HF RADAR; WAVE MODEL
NORA Subject Terms: Marine Sciences
Date made live: 16 Mar 2009 12:55 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/6495

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...